Reproducible
data analysis

Reproducible research in a context

Providing a reproducible analysis is the most important and easiest aspect of open science

• data cannot always be easily shared
• analysis connects data and results
• confirming pre-registered hypotheses is less valuable without it
• pre-registration is often not possible or useful ¹

Why reproducible research?

• builds trust
• reduces errors
• makes it easier to write the method section ¹
• streamlines manuscript writing

Levels of reproducible data analysis

1. Script all analysis steps
2. Version control software & public repositories for version control & publication
3. Scientific and technical publishing system for documenting and implementing analysis pipeline
4. Scientific and technical publishing system for writing papers

Tools for reproducible data analysis

• or any other programming language
• or other IDEs with git/github integration
• , or other version control software
• , or other scientific and technical publishing systems

Scripting

Different scripts for

• utility functions¹
• data cleaning and preparation²
• running analyses ³
• preparing analysis results for the manuscript

Working around slow analysis parts

fn = "my_analysis_results.Rdata"

if (file.exists(fn)) {
  load(fn)
} else {
  # make my analysis
  save(my_fit, file = fn)
}

Version control

Start each paper by setting up a (git/github) project

• first step to later publishing the code
• version control easily allows to go back to older versions of the analysis
• saves as backup method ¹
• allows to work jointly on a project
• is well integrated with git/github (show)

Document analysis pipeline in Rmarkdown

• One R-markdown document ¹ describes
  • data cleaning
  • statistical analysis
  • supplementary analyses
  • generation of statistics for paper